In computing, a distributed file system (DFS) or network file system is any file system that allows access to files from multiple hosts sharing via a computer network. This makes it possible for multiple users on multiple machines to share files and storage resources.
A Distributed File System(DFS) is simply a classical model of a file system distributed across multiple machines.The purpose is to promote sharing of dispersed files.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
In computing, a distributed file system (DFS) or network file system is any file system that allows access to files from multiple hosts sharing via a computer network. This makes it possible for multiple users on multiple machines to share files and storage resources.
A Distributed File System(DFS) is simply a classical model of a file system distributed across multiple machines.The purpose is to promote sharing of dispersed files.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
2. Achievements through DFS
Two important goals of distributed file systems
Network Transparency
To provide the same functional capabilities to access files distributed over a network
Users do not have to be aware of the location of files to access them
High Availability
Users should have the same easy access to files, irrespective of their physical
location
System failures or regularly scheduled activities such as backups or maintenance
should not result in the unavailability of files
3. Architecture
Files can be stored at any machine and computation can be
performed at any machine
A machine can access a file stored on a remote machine where
the file access operations are performed and the data is returned
Alternatively, File Servers are provided as dedicated to storing
files and performing storage and retrieval operations
Two most important services in a DFS are
Name Server: a process that maps names specified by clients
to stored objects, e.g. files and directories
Cache Manager: a process that implements file caching, i.e.
copying a remote file to the client’s machine when referred by
the client
6. Mechanisms for Building DFS
Mounting
Allows the binding together of different filename spaces to form a single
hierarchically structured name space
Kernel maintains a structure called the mount table which maps mount
points to appropriate storage devices.
Caching
To reduce delays in the accessing of data by exploiting the temporal locality
of reference exhibited by program
Hints
An alternative to cached data to overcome inconsistency problem when
multiple clients access shared data
Bulk Data Transfer
To overcome the high cost of executing communication protocols, i.e.
assembly/disassembly of packets, copying of buffers between layers
Encryption
To enforce security in distributed systems with a scenario that two entities
wishing to communicate establish a key for conversation
7. Design Goals
Naming and Name Resolution
Caches on Disk or Main Memory
Writing Policy
Cache Consistency
Availability
Scalability
Semantics
8. Naming and Name Resolution
Name in file systems is associated with an object (e.g. a file or a directory)
Name resolution refers to the process of mapping a name to an object, or in case
of replication, to multiple objects.
Name space is a collection of names which may or may not share an identical
resolution mechanism
Three approaches to name files in DE
Concatenation
Mounting (Sun NFS)
Directory structured (Sprite and Apollo)
The Concepts of Contexts
A context identifies the name space in which to resolve a given name
Examples: x-Kernel Logical File System, Tilde Naming Scheme
Name Server
Resolves the names in distributed systems. Drawbacks involved such as single
point of failure, performance bottleneck. Alternate is to have several name
servers, e.g. Domain Name Servers
9. Caches on Disk or Main Memory
Cache in Main Memory
Diskless workstations can also take advantage of caching
Accessing a cache is much faster than access a cache on local disk
The server-cache is in the main memory, and hence a single cache design for
both
Disadvantages
It competes with the virtual memory system for physical memory space
A more complex cache manager and memory management system
Large files cannot be cached completely in memory
Cache in Local Disk
Large files can be cached without affecting performance
Virtual memory management is simple
Example: Coda File System
10. Writing Policy
Decision to when the modified cache block at a client should be
transferred to the server
Write-through policy
All writes requested by the applications at clients are also carried out at
the server immediately.
Delayed writing policy
Modifications due to a write are reflected at the server after some delay.
Write on close policy
The updating of the files at the server is not done until the file is closed
11. Cache Consistency
Two approaches to guarantee that the data returned to the client
is valid.
Server-initiated approach
Server inform cache managers whenever the data in the client caches become
stale
Cache managers at clients can then retrieve the new data or invalidate the
blocks containing the old data
Client-initiated approach
The responsibility of the cache managers at the clients to validate data with the
server before returning it
Both are expensive since communication cost is high
Concurrent-write sharing approach
A file is open at multiple clients and at least one has it open for writing.
When this occurs for a file, the file server informs all the clients to purge their
cached data items belonging to that file.
Sequential-write sharing issues causing cache inconsistency
Client opens a file, it may have outdated blocks in its cache
Client opens a file, the current data block may still be in another client’s cache
waiting to be flushed. (e.g. happens in Delayed writing policy)
12. Availability
Immunity to the failure of server of the communication network
Replication is used for enhancing the availability of files at different
servers
It is expensive because
Extra storage space required
The overhead incurred in maintaining all the replicas up to date
Issues involve
How to keep the replicas of a file consistent
How to detect inconsistencies among replicas of a file and recover from these
inconsistencies
Causes of Inconsistency
A replica is not updated due to failure of server
All the file servers are not reachable from all the clients due to network
partition
The replicas of a file in different partitions are updated differently
13. Availability (contd.)
Unit of Replication
The most basic unit is a file
A group of files of a single user or the files that are in a server
(the group file is referred to as volume, e.g. Coda)
Combination of two techniques, as in Locus
Replica Management
The maintenance of replicas and in making use of them to
provide increased availability
Concerns with the consistency among replicas
A weighted voting scheme (e.g. Roe File System)
Designated agents scheme (e.g. Locus)
Backups servers scheme (e.g. Harp File System)
14. Scalability
The suitability of the design of a system to cater to the demands
of a growing system
As the system grow larger, both the size of the server state and
the load due to invalidations increase
The structure of the server process also plays a major role in
deciding how many clients a server can support
If the server is designed with a single process, then many clients have to
wait for a long time whenever a disk I/O is initiated
These waits can be avoided if a separate process is assigned to each
client
A significant overhead due to the frequent context switches to handle
requests from different clients can slow down the server
An alternate is to use Lightweight processes (threads)
15. Semantics
The semantics of a file system characterizes the effects of accesses on
files
Guaranteeing the semantics in distributed file systems, which employ
caching, is difficult and expensive
In server-initiated cache the invalidation may not occur immediately
after updates and before reads occur at clients.
This is due to communication delays
To guarantee the above semantics all the reads and writes from various
clients will have to go through the server
Or sharing will have to be disallowed either by the server, or by the use
of locks by applications